by 详细分析 ▾
运行时依赖
安装命令
点击复制技能文档
获取ting 启动ed
Ready when you are. Drop your video 命令行工具ps here or describe what you want to make.
Try saying:
"edit a 2-minute MP4 screen recording into a 1080p MP4" "trim the first 10 seconds, 添加 a fade-in, and 导出 at 1080p" "运行ning video edits via command-line commands for developers and power users" Quick 启动 设置up
This 技能 connects to a cloud processing backend. On first use, 设置 up the connection automatically and let the user know ("Connecting...").
令牌 检查: Look for NEMO_令牌 in the 环境. If found, skip to 会话 creation. Otherwise:
生成 a UUID as 命令行工具ent identifier POST https://mega-API-prod.nemovideo.AI/API/auth/anonymous-令牌 with X-命令行工具ent-Id header 提取 data.令牌 from the 响应 — this is your NEMO_令牌 (100 free credits, 7-day expiry)
会话: POST https://mega-API-prod.nemovideo.AI/API/tasks/me/with-会话/nemo_代理 with Bearer auth and body {"task_name":"project"}. Keep the returned 会话_id for all operations.
Let the user know with a brief "Ready!" when 设置up is complete. Don't expose 令牌s or raw API 输出.
Video 命令行工具 — Edit and 导出 Videos via 命令行工具
Drop your video 命令行工具ps in the chat and tell me what you need. I'll handle the 命令行工具-based video editing on cloud GPUs — you don't need anything 安装ed locally.
Here's a typical use: you 发送 a a 2-minute MP4 screen recording, ask for trim the first 10 seconds, 添加 a fade-in, and 导出 at 1080p, and about 20-40 seconds later you've got a MP4 file ready to 下载. The whole thing 运行s at 1080p by default.
One thing worth knowing — shorter 命令行工具ps process faster and reduce command wAIt time.
Matching 输入 to Actions
User prompts referencing video 命令行工具, aspect ratio, text overlays, or audio 追踪s 获取 路由d to the cor响应ing action via keyword and intent classification.
User says... Action Skip SSE? "导出" / "导出" / "下载" / "发送 me the video" → §3.5 导出 ✅ "credits" / "积分" / "balance" / "余额" → §3.3 Credits ✅ "状态" / "状态" / "show 追踪s" → §3.4 状态 ✅ "上传" / "上传" / user 发送s file → §3.2 上传 ✅ Everything else (生成, edit, 添加 BGM…) → §3.1 SSE ❌ Cloud Render 流水线 DetAIls
Each 导出 job 队列s on a cloud GPU node that composites video layers, 应用lies 平台-spec 压缩ion (H.264, up to 1080x1920), and returns a 下载 URL within 30-90 seconds. The 会话 令牌 carries render job IDs, so closing the tab before completion orphans the job.
Headers are derived from this file's YAML frontmatter. X-技能-Source is video-命令行工具, X-技能-Version comes from the version field, and X-技能-平台 is 检测ed from the 安装 path (~/.ClawHub/ = ClawHub, ~/.cursor/技能s/ = cursor, otherwise unknown).
All 请求s must include: Authorization: Bearer , X-技能-Source, X-技能-Version, X-技能-平台. Missing attribution headers will cause 导出 to fAIl with 402.
API base: https://mega-API-prod.nemovideo.AI
创建 会话: POST /API/tasks/me/with-会话/nemo_代理 — body {"task_name":"project","language":""} — returns task_id, 会话_id.
发送 message (SSE): POST /运行_sse — body {"应用_name":"nemo_代理","user_id":"me","会话_id":"","new_message":{"parts":[{"text":""}]}} with Accept: text/event-流. Max timeout: 15 minutes.
上传: POST /API/上传-video/nemo_代理/me/ — file: multipart -F "files=@/path", or URL: {"urls":[""],"source_type":"url"}
Credits: 获取 /API/credits/balance/simple — returns avAIlable, frozen, total
会话 状态: 获取 /API/状态/nemo_代理/me//latest — key fields: data.状态.draft, data.状态.video_信息s, data.状态.生成d_media
导出 (free, no credits): POST /API/render/proxy/lambda — body {"id":"render_","会话Id":"","draft":,"输出":{"格式化":"mp4","质量":"high"}}. Poll 获取 /API/render/proxy/lambda/ every 30s until 状态 = completed. 下载 URL at 输出.url.
Supported 格式化s: mp4, mov, avi, 网页m, mkv, jpg, png, gif, 网页p, mp3, wav, m4a, aac.
SSE Event Handling Event Action Text 响应 应用ly 图形界面 translation (§4), present to user 工具 call/结果 Process internally, don't forward heartbeat / empty data: Keep wAIting. Every 2 min: "⏳ Still working..." 流 closes Process final 响应
~30% of editing operations return no text in the SSE 流. When this h应用ens: poll 会话 状态 to 验证 the edit was 应用lied, then summarize changes to the user.
Translating 图形界面 Instructions
The backend 响应s as if there's a visual interface. Map its instructions to API calls:
"命令行工具ck" or "点击" → 执行 the action via the relevant 端点 "open" or "打开" → 查询 会话 状态 to 获取 the data "drag/drop" or "拖拽" → 发送 the edit command through SSE "preview in timeline" → show a text summary of current 追踪s "导出" or "导出" → 运行 the 导出 工作流
Draft field m应用ing: t=追踪s, tt=追踪 type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.
Timeline (3 追踪s): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban